Imperialist Competitive Algorithm (ICA) Approach for Optimization of the Surface Grinding Process

Journal of Modern Processes in Manufacturing and Production

مقاله 5، دوره 9، شماره 1، اردیبهشت 2020، صفحه 51-62   

نوع مقاله: Research Paper

نویسندگان

Ahmad Afsari^{* 1}؛ Mohammad Ramezani¹؛ Shahin Heidari²؛ Jafar Karimi¹

¹Department of Mechanical Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran

²Bone and Joint Diseases Research Center, Shiraz University of Medical Sciences, Shiraz, Iran

چکیده

The imperialist Competitive Algorithm (ICA) is one of the recent meta-heuristic algorithms proposed to solve optimization problems. The Imperialist Competitive Algorithm is based on a socio-politically inspired optimization strategy. This paper presents an Imperialist Competitive Algorithm (ICA) to optimize the performance of a surface grinding operation.  Moreover, the multi-objective optimization of a surface grinding process is suggested by using an evolutionary algorithm. Factors like depth of dressing, lead of dressing, workpiece speed and wheel speed are considered to minimize the production cost, surface roughness and to maximize the production rate. The suggested approach presents two constraints handling techniques: constraints handling strategy of ICA and penalty function method. The effectiveness of this algorithm for grinding operation is investigated by comparing the results to other algorithms available in the literature. Results show that the proposed algorithm in this work gives a better performance in a shorter time for the optimization of machining parameters in comparison to other works.

کلیدواژه‌ها

Machining Parameters؛ Optimization؛ Surface Grinding؛ Imperialist Competitive Algorithm (ICA)

مراجع

[1] Yang, Y., Li, X. and Gao, L., 2013. June. Parameters optimization of a multi-pass milling process based on imperialist competitive algorithm. In Proceedings of the 2013 IEEE 17th International Conference on Computer Supported Cooperative Work in Design (CSCWD). 406-410.

[2] Zhang, G., Liu, M., Li, J., Ming, W., Shao, X. and Huang, Y., 2014. Multi-objective optimization for surface grinding process using a hybrid particle swarm optimization algorithm. The International Journal of Advanced Manufacturing Technology. 71(9-12):1861-1872.

[3] Wen, X.M., Tay, A.A.O. and Nee, A.Y.C. 1992. Micro-computer-based optimization of the surface grinding process. Journal of Materials Processing Technology. 29(1-3):75-90.

[4] Amitay, G., Malkin, S. and Koren, Y. 1981. Adaptive control optimization of grinding.

[5] Malkin, S. 1985. November. Practical approaches to grinding optimization. In Milton. C. Shaw Grinding Symposium, ASME Winter Annual Meeting. Miami Beach, FL. 289-299.

[6] Saravanan, R., Asokan, P. and Sachidanandam, M. 2002. A multi-objective genetic algorithm (GA) approach for optimization of surface grinding operations. International journal of machine tools and manufacture. 42(12):1327-1334.

[7] Baskar, N., Saravanan, R., Asokan, P. and Prabhaharan, G. 2004. Ants colony algorithm approach for multi-objective optimisation of surface grinding operations. The International Journal of Advanced Manufacturing Technology. 23(5-6):311-317.

[8] Lee, K.M., Hsu, M.R., Chou, J.H. and Guo, C.Y. 2011. Improved differential evolution approach for optimization of surface grinding process. Expert Systems with Applications. 38(5): 5680-5686.

[9] Krishna, A.G. 2007. Retracted: optimization of surface grinding operations using a differential evolution approach.

[10] Pawar, P.J., Rao, R.V. and Davim, J.P. 2010. Multiobjective optimization of grinding process parameters using particle swarm optimization algorithm. Materials and manufacturing processes. 25(6): 424-431.

[11] Krishna, A.G. and Rao, K.M. 2006. Multi-objective optimisation of surface grinding operations using scatter search approach. The International Journal of Advanced Manufacturing Technology. 29(5-6): 475-480.

[12] Atashpaz-Gargari, E. and Lucas, C. 2007. September. Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. IEEE congress on evolutionary computation. 4661-4667.

[13]  Yazdipour, A. and Ghaderi, M.R. 2014. Optimization of weld bead geometry in GTAW of CP titanium using imperialist competitive algorithm. The International Journal of Advanced Manufacturing Technology. 72(5-8): 619-625.

[14] Shabani, M.O. and Mazahery, A. 2012. The GA optimization performance in the microstructure and mechanical properties of MMNCs. Transactions of the Indian Institute of Metals. 65(1):77-83.

[15]  Ulas, H.B. 2014. Experimental determination of cutting forces and surface roughness when turning 50CrV4 steel (SAE 6150) and modelling with the artificial neural network approach. Transactions of the Indian Institute of Metals. 67(6): 869-879.

[16] Lucas, C., Nasiri-Gheidari, Z. and Tootoonchian, F. 2010. Application of an imperialist competitive algorithm to the design of a linear induction motor. Energy conversion and management, 51(7): 1407-1411.

[17] Gargari, E.A., Hashemzadeh, F., Rajabioun, R. and Lucas, C. 2008. A novel approach for PID controller design in MIMO distillation column process. International Journal of Intelligent Computing and Cybernetics. 1(3):337-355.

[18] Kamarian, S. 2013. Volume fraction optimization of four-parameter FGM beams resting on elastic foundation. Journal of Advanced Design and Manufacturing Technology. 6(4): 75-82.

[19] Towsyfyan, H., ADNANI, S.S. and Rashidian, R. 2014. Application of Imperialist Competitive Algorithm to optimization problems arising in welding process. Journal of Advanced Design and Manufacturing Technology.  7(3):65-72.

[20] Norouzi, G.S. and Akbarzadeh, A.R. 2015. Prismatic Series Elastic Actuator: Modelling and Control by ICA and PSO-Tuned Fractional Order PID. Journal of Advanced Design and Manufacturing Technology. 8(4):23-32.

[21] Fadaei, A., Gorbanpour, A. and Salarpour, A. 2016. Optimizing the Parameters Contributing to Riveting Quality Using Imperialist Competitive Algorithm and Predicting the Objective Function via the Three Models MLR, RBF, and ANN-GA. Journal of Advanced Design and Manufacturing Technology. 9(1):1-12.